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Methodology of the automatic generalization of buildings, road networks, forests and surface waters: a case study based on the Topographic Objects Database in Poland / Izabela Karsznia in Geocarto international, vol 35 n° 7 ([15/05/2020])
[article]
Titre : Methodology of the automatic generalization of buildings, road networks, forests and surface waters: a case study based on the Topographic Objects Database in Poland Type de document : Article/Communication Auteurs : Izabela Karsznia, Auteur ; Marta Przychodzeń, Auteur ; Karolina Sielicka, Auteur Année de publication : 2020 Article en page(s) : pp 735 - 758 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] ArcGIS
[Termes IGN] base de connaissances
[Termes IGN] base de données orientée objet
[Termes IGN] bâtiment
[Termes IGN] données topographiques
[Termes IGN] eau de surface
[Termes IGN] forêt
[Termes IGN] placement automatique des objets
[Termes IGN] Pologne
[Termes IGN] réseau routier
[Vedettes matières IGN] GénéralisationRésumé : (auteur) This research presents the formalization and verification of the methodology for the automatic generalization of buildings, road networks, forests and surface waters from the Topographic Objects Database (BDOT10k) in Poland. The article makes the following contributions. First, the generalization methodology contained in the official documents was acquired and presented in the form of the knowledge base. Second, the possibilities and limitations of the implementation of the knowledge base in ArcGIS were discussed. Third, the suitability of the BDOT10k structure for the purpose of automatic generalization performance was verified. As a result of the conducted generalization tests, it was found that the formalization and implementation of the methodology contained in the official specifications, in the automatic mode are not entirely possible. The generalization results, however, are promising. The presented research is in line with the studies recently conducted not only by Polish but also other European National Mapping Agencies. Numéro de notice : A2020-271 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1533591 Date de publication en ligne : 03/12/2018 En ligne : https://doi.org/10.1080/10106049.2018.1533591 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95055
in Geocarto international > vol 35 n° 7 [15/05/2020] . - pp 735 - 758[article]Comment cartographier l’occupation du sol en vue de modéliser les réseaux écologiques ? Méthodologie générale et cas d’étude en Île-de-France / Chloé Thierry in Sciences, eaux & territoires, article hors-série n° 65 (mai 2020)
[article]
Titre : Comment cartographier l’occupation du sol en vue de modéliser les réseaux écologiques ? Méthodologie générale et cas d’étude en Île-de-France Type de document : Article/Communication Auteurs : Chloé Thierry, Auteur ; Nicolas Lesieur-Maquin, Auteur ; Cindy Fournier, Auteur ; et al., Auteur Année de publication : 2020 Note générale : bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Cartographie
[Termes IGN] aide à la décision
[Termes IGN] base de données cartographiques
[Termes IGN] BD ortho
[Termes IGN] BD Topo
[Termes IGN] biodiversité
[Termes IGN] carte d'occupation du sol
[Termes IGN] couche thématique
[Termes IGN] données écologiques
[Termes IGN] écosystème
[Termes IGN] Ile-de-France
[Termes IGN] SCAN25
[Termes IGN] théorie des graphes
[Termes IGN] trame verte et bleue
[Termes IGN] zone tamponRésumé : (éditeur) Une cartographie de l’occupation du sol est souvent essentielle aux décideurs et gestionnaires d’espace pour appréhender les enjeux de maintien et de restauration des continuités écologiques favorables au maintien de la biodiversité. Dans cet article, les auteurs présentent une démarche méthodologique qui, à partir des différentes bases de données cartographiques disponibles, a permis de réaliser une cartographie précise de l’occupation du sol pour mieux étudier la connectivité des espaces naturels sur le territoire fortement urbanisé de la région Île-de-France. Numéro de notice : A2020-353 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtSansCL DOI : 10.14758/SET-REVUE.2020.HS.05 Date de publication en ligne : 01/05/2020 En ligne : https://doi.org/10.14758/SET-REVUE.2020.HS.05 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95237
in Sciences, eaux & territoires > article hors-série n° 65 (mai 2020)[article]A convolutional neural network with mapping layers for hyperspectral image classification / Rui Li in IEEE Transactions on geoscience and remote sensing, vol 58 n° 5 (May 2020)
[article]
Titre : A convolutional neural network with mapping layers for hyperspectral image classification Type de document : Article/Communication Auteurs : Rui Li, Auteur ; Zhibin Pan, Auteur ; Yang Wang, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 3136 - 3147 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] algèbre linéaire
[Termes IGN] analyse discriminante
[Termes IGN] analyse en composantes principales
[Termes IGN] analyse multidimensionnelle
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] couche thématique
[Termes IGN] dispersion
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image hyperspectrale
[Termes IGN] réductionRésumé : (auteur) In this article, we propose a convolutional neural network with mapping layers (MCNN) for hyperspectral image (HSI) classification. The proposed mapping layers map the input patch into a low-dimensional subspace by multilinear algebra. We use our mapping layers to reduce the spectral and spatial redundancies and maintain most energy of the input. The feature extracted by our mapping layers can also reduce the number of following convolutional layers for feature extraction. Our MCNN architecture avoids the declining accuracy with increasing layers phenomenon of deep learning models for HSI classification and also saves the training time for its effective mapping layers. Furthermore, we impose the 3-D convolutional kernel on the convolutional layer to extract the spectral–spatial features for HSI. We tested our MCNN on three data sets of Indian Pines, University of Pavia, and Salinas, and we achieved the classification accuracy of 98.3%, 99.5%, and 99.3%, respectively. Experimental results demonstrate that the proposed MCNN can significantly improve classification accuracy and save much time consumption. Numéro de notice : A2020-234 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2948865 Date de publication en ligne : 12/11/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2948865 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94980
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 5 (May 2020) . - pp 3136 - 3147[article]Deep learning for enrichment of vector spatial databases: Application to highway interchange / Guillaume Touya in ACM Transactions on spatial algorithms and systems, TOSAS, vol 6 n° 3 (May 2020)
[article]
Titre : Deep learning for enrichment of vector spatial databases: Application to highway interchange Type de document : Article/Communication Auteurs : Guillaume Touya , Auteur ; Imran Lokhat , Auteur Année de publication : 2020 Projets : 1-Pas de projet / Article en page(s) : 21 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] apprentissage profond
[Termes IGN] base de données vectorielles
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] échangeur routier
[Termes IGN] enrichissement sémantique
[Termes IGN] reconnaissance d'objets
[Termes IGN] segmentation d'imageRésumé : (auteur) Spatial analysis and pattern recognition with vector spatial data is particularly useful to enrich raw data. In road networks, for instance, there are many patterns and structures that are implicit with only road line features, among which highway interchange appeared very complex to recognize with vector-based techniques. The goal is to find the roads that belong to an interchange, such as the slip roads and the highway roads connected to the slip roads. To go further than state-of-the-art vector-based techniques, this article proposes to use raster-based deep learning techniques to recognize highway interchanges. The contribution of this work is to study how to optimally convert vector data into small images suitable for state-of-the-art deep learning models. Image classification with a convolutional neural network (i.e., is there an interchange in this image or not?) and image segmentation with a u-net (i.e., find the pixels that cover the interchange) are experimented and give better results than existing vector-based techniques in this specific use case (99.5% against 74%). Numéro de notice : A2020-365 Affiliation des auteurs : LASTIG COGIT (2012-2019) Autre URL associée : vers HAL Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1145/3382080 Date de publication en ligne : 01/04/2020 En ligne : https://doi.org/10.1145/3382080 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95399
in ACM Transactions on spatial algorithms and systems, TOSAS > vol 6 n° 3 (May 2020) . - 21 p.[article]Documents numériques
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Deep learning for enrichment of vector spatial databases ... - preprintAdobe Acrobat PDF Delineating and modeling activity space using geotagged social media data / Lingqian Hu in Cartography and Geographic Information Science, vol 47 n° 3 (May 2020)
[article]
Titre : Delineating and modeling activity space using geotagged social media data Type de document : Article/Communication Auteurs : Lingqian Hu, Auteur ; Zhenhong Li, Auteur ; Xinyue Ye, Auteur Année de publication : 2020 Article en page(s) : pp 277 - 288 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] distance
[Termes IGN] données localisées des bénévoles
[Termes IGN] données massives
[Termes IGN] données socio-économiques
[Termes IGN] logement
[Termes IGN] loisir
[Termes IGN] Los Angeles
[Termes IGN] quartier
[Termes IGN] réseau social
[Termes IGN] sport
[Termes IGN] Twitter
[Termes IGN] voisinage (relation topologique)
[Termes IGN] zone urbaineRésumé : (auteur) It has become increasingly important in spatial equity studies to understand activity spaces – where people conduct regular out-of-home activities. Big data can advance the identification of activity spaces and the understanding of spatial equity. Using the Los Angeles metropolitan area for the case study, this paper employs geotagged Twitter data to delineate activity spaces with two spatial measures: first, the average distance between users’ home location and activity locations; and second, the area covered between home and activity locations. The paper also finds significant relationship between the spatial measures of activity spaces and neighborhood spatial and socioeconomic characteristics. This research enriches the literature that aims to address spatial equity in activity spaces and demonstrates the applicability of big data in urban socio-spatial research. Numéro de notice : A2020-135 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2019.1705187 Date de publication en ligne : 10/02/2020 En ligne : https://doi.org/10.1080/15230406.2019.1705187 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94843
in Cartography and Geographic Information Science > vol 47 n° 3 (May 2020) . - pp 277 - 288[article]Réservation
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